code: def test_probability_larger(self):
      data = np.array([-10, -0.1, 0.0, 0, 1.5, 3.4])
  
      # ============== Without replace value
      # Filter positive number
      modified_data, idxes = filtering.filter_proba(data, 3.3)
      np.testing.assert_equal(modified_data, np.array([3.4]))
      np.testing.assert_equal(idxes, np.array([5]))
      # Filter zero
      modified_data, idxes = filtering.filter_proba(data, 0)
      np.testing.assert_equal(
          modified_data, np.array([0.0, 0.0, 1.5, 3.4])
      )
      np.testing.assert_equal(idxes, np.array([2, 3, 4, 5]))
      # Filter negative number
      modified_data, idxes = filtering.filter_proba(data, -5)
      np.testing.assert_equal(
          modified_data, np.array([-0.1, 0.0, 0.0, 1.5, 3.4])
      )
      np.testing.assert_equal(idxes, np.array([1, 2, 3, 4, 5]))
  
      # ============== With replace value
      # Filter positive number
      modified_data, idxes = filtering.filter_proba(
          data, 2, replace=-99
      )
      np.testing.assert_equal(
          modified_data[modified_data > 2], np.array([3.4])
      )
      np.testing.assert_equal(idxes, np.array([5]))
      # Filter zero
      modified_data, idxes = filtering.filter_proba(
          data, 0, replace=-99
      )
      np.testing.assert_equal(
          modified_data, np.array([-99, -99, -99, -99, 1.5, 3.4])
      )
      np.testing.assert_equal(idxes, np.array([4, 5]))
      # Filter negative number
      modified_data, idxes = filtering.filter_proba(
          data, -5, replace=-99
      )
      np.testing.assert_equal(
          modified_data, np.array([-99, -0.1, 0.0, 0.0, 1.5, 3.4])
      )
      np.testing.assert_equal(idxes, np.array([1, 2, 3, 4, 5]))
  
file_path: nlpaug/test/util/selection/test_filtering.py
imports: import unittest
  import numpy
  import nlpaug.util.selection.filtering
label_desc: Use image node_id 1 for calling the TestFiltering obj's underlying member method code with example usage: obj.test_probability_larger() without return types